The Effect of Sleep Quality and IQ on Memory Dissertation

Background

The relationship between sleep and memory has been a matter of increased interest for scientists around the globe. Sleep and memory disturbances have a great connection because the impact of memory disturbances on sleep (Stickgold & Walker, 2005). Sleep does not indicate that all body organs go to rest, some organs like the heart and brain are always active (Biebuyck & Lydic. 2013). However, their metabolism is lowered since the body does not require much energy because some organs are still resting. As such, sleep plays a critical role in maintaining the metabolic activities in the human body. There is a constant flow of activities in the body even when the body is resting. Therefore, the major aim of sleep is to balance the energies in the body.

One’s memory depends on one’s ability to grasp what they have encountered. Thus, a well-rested memory is likely to influence their ability to understand the context they have been subjected. The brain determines the ability to remember any activity (Jo et al., 2019). However, the nature of the activity that an individual is exposed to determines the rate of memory capture. An individual is likely to remember activities based on their impacts. More significantly, there is a higher likelihood that an individual will remember most of the activities that are either good or bad (Tucker et al., 2020). The average activities that will not have an impact on the individual are likely to be forgotten. Therefore, the context of the activities an individual is exposed to plays an essential role in determining how they remember the activities that affect their memory capture (Grandner, 2017).

Memory is associated with long-term ability to remember what the brain has taken. This activity requires a process of consolidation determined by the amount of sleep one receives (Tucker et al., 2020). An individual will take some time to digest what happens and finally remember or store it for future reference. However, the individual does not have to use it in the future. There are some of the things that are stored in the brain and are not remembered. Though when such events happen there is a higher likelihood that they will be remembered.

However, sleep is determined by a number of factors such as period and conform which determined the quality of sleep. Averagely, adults have 6-8 hours of sleep per day with minors having up to 14 hours of sleep per day (Medic et al., 2017). This sleeping period does not determine the quality of sleep and rest of organ. Age has been one of the factors that affect the quality of sleep. The nature of sleeping patterns have an impact on memory. For example, at a tender age children will have so much rest and have limited amount that they can remember about their lives in the future. Though the adults will have a few hours of rest and still have more that can be remembered. As such, the quality of sleep is impacted by the age but it has no significant impact on the life of the individual at the end. Thus, the ability to grasp concepts and phenomena contribute to ability to memorize and idea.

Further, stress contribute to amount of sleep and ability to memorize something. Sleep and stress have dependent relationship where one affects the other. Stress can affect the amount and quality of sleep altering the pattern of sleep (Ashton & Cairney, 2021). When the pattern of sleep is altered, it will change the level of stress hormones further affecting sleep. During the cases when people are stressed, there is a higher likelihood that they will have limited ability to create the time for sleep. This implies that they are likely going to have limited time to sleep. The sleeping patterns are likely to have a more impact on the memory capacity to grasp. When sleep is disturbed, it will affect the memorization ability hence creating a relationship between stress, sleep and memory.

A good sleep will help organs rest, but the brain remains functional. During this time, the brain processes information that it has gathered during the day and transforming it into memories. From the above discussion, all studies confirm the brain creates memories especially when people sleep. However, it will also depend on the conditions of the brain and other factors that affect reasoning and sleep such as stress (Yang et al., 2022). Thus, through the various facts affirmed by the many research studies, this study will mainly be focusing on the impacts of the sleep on the memory recollection.

Problem Statement

The increase in the number of people affected by the memory loss conditions has increased the significance of this research. More especially, the memory conditions and sleeping patterns is an area which has not been covered well as per the studies that have been conducted (Luongo et al., 2021). The major aim has been to create the perfect ways of solving the key issues affecting memory. As such, by assessing the nature of sleep and the patterns in relation to memory there will be an essential subject of discussion in the globally economy. Therefore, this research plays a critical role in highlighting the key factors affecting the quality of sleep.

There are various studies that explain sleeping, brain and memory. However, they discuss the functionality of this subjects creating a minimal relationship between sleep and memory.

Other studies highlight cause of poor sleep and memory and how it could be improved. The causes of poor sleep have been well captured in many research studies which have been conducted. The major challenge or gap exposed is the key relationship and how various factors affecting the relationship are highly related in the core units of the studies. For a long time that this topic has been underscored, more significantly through the increases in the cases of memory loss in the modern society. Memory loss is a condition that is hardly realized by many people. This causes are highly linked with the relationship between memory recollection and sleeping patterns. Therefore, this core relationship plays a significant role in most of the activities that are associated with memory and sleeping patterns.

This research discusses relationship between sleep and memory which other studies have failed to cover. The core of this relationship is to efficiently determine the major factors that affect memory recollection by assessing the sleeping patterns. Further, the study will address how it will be effective and applicable in improving people’s memories. The various factors that affect memory retention capacity will be discussed as a way of determining the various segments of this research. There key relationship and correlation will be effectively determined in the process.

Lastly it will address challenges faced when assessing this phenomena, and other gaps created by other studies in explaining the relationship between sleep and memory. More particularly, the areas that need further research will be addressed to cover the key issues in the society as per the memory and sleep patterns relationship. The research will cover this essential section as a global issue. Therefore, the various factors as covered by other research studies will be determined as a way of securing the best remedies.

Objectives, Research Questions, and Hypotheses

The research study will be conducted by following the key objectives. The objectives will help the research in creating the research questions as well as the themes that will be applied during data analysis. Therefore, the following are the key objectives of this study;

1. Conduct a holistic literature review concerning the effect of sleep and IQ on memory.
2. Assess the relationship between sleep and memory retention capacity.
3. Explore how IQ affects memory retention capacity

Three major research questions were created to help the researcher come up with the various ways of coming up with better recommendations. The research questions were created from the objectives and aims of the research paper. Thus, the following are the major questions of the research paper;

1. What is the relationship between IQ and memory retention?
2. How does the quantity of sleep affect memory retention?
3. How does sleeping quality influence memory retention?

Hypothesis plays a significant role in a research paper. For instance, this research paper has both the null and alterative hypothesis. The hypothesis will be applied in data collection and further analysis in the research paper. This paper will test a total of three hypotheses listed below.

• H1₀: There is no correlation between sleeping quantity and memory retention.
• H1_A: There is a positive correlation between sleeping quantity and memory retention.
• H2₀: There is no correlation between sleeping quality and memory retention.
• H2_A: There is a positive correlation between sleeping quality and memory retention.
• H3₀: There is no correlation between IQ and memory retention.
• H3_A: There is a positive correlation IQ quality and memory retention.

Significance of the Study

The study will play a critical role in assessing all the required sections that have not been discussed in other research studies. As such, the following benefits will give the study a first priority among many other required sections in the research.

1. Sleep is an important aspect in every living animal’s life because it aids in resting. This study explains helps the science community and the public to understand sleep and how it positively contributes to people’s life.
2. This study will contribute to the existing literature that explain the relationship between sleep, memory and other factors that contribute to both such as stress.
3. The study attempts to explain how learning is connected to sleep and memory. Sleep- deprived individual cannot concentrate for long on a given time which is the determinant of learning. Further, memory helps in recalling something learn over the past period thus connecting sleep and memory to learning.
4. This study closes down on inconsistence in previous research explaining sleep and memory.
5. Lastly, this study aids future research by giving suggestions on what should be done to improve the literature and get better results on this topic.

Review of Existing Literature

Central Concepts

Sleep is part of physiological processes in human anatomy where neurobiological regulations that contribute to normal functioning of human being. Sleep is identified as unavoidable physiological process in humans’ life and it happens daily with human spending averagely 20-40% of the day sleeping (Chaput et al., 2018). If this pattern is broken, it may lead to health problems and inability to function normally in many aspects (Chaput et al., 2018). Sleep is a recurring state in human anatomy and it is characterized by altered conscious, inhibiting sensory activities. The inhibition reduces the muscle activates during the rapid eye movement also known as sleep lowering the interaction with the environment (Burton & Walker, 2020).

Previous studies have confirmed that sleep helps restring physical and mental health since, all body organs and muscles relax hence resting. However, there are various important organs that do not stop functioning such as lungs, kidney, heart and brain (Krueger, 2020). In this resting state, the body’s’ metabolism is lowered allowing body organs to recharge their energy.

Sleep is a complex anatomy from the body which involves the brain. The hypothalamus part of the brain controls sleeps since it contains nerves cells that acts a control center of the body responsible for sleep and arousal. The brain contains suprachiasmatic nucleus (SCN) which are clusters of cells that receive information on light intensity heling the body program its sleep (Silvani, 2021). This explains why people are likely to fall asleep in a dark environment. Despite their inability to see, blind people also are able to maintain their sensitivity to light due to SCN.

The brains stem coordinates with the hypothalamus to control the transition from sleep and waking. The sleeping cells within the brain produce the arousal chemicals called GABA which can be reduced or increased depending on the state of the mind between waking up and sleeping (Stickgold & Walker, 2005). Contextually, sleep is controlled by the brains since the hypothalamus, brainstem, thalamus, cerebral cortex, pineal gland, basal forebrain, midbrain and amygdala coordinate to coordinate between sleeping and waking up (Stickgold & Walker, 2005). In summary, sleep is a complicated process involved in restoring the nervous system, which allows the body to remain healthy both physically and psychologically. This study defines sleep as a recurring state of body and mind associated with relative inactivity of the nervous system, relaxation of muscles’, and almost complete lack of consciousness.

Sleep quality is a controversial concept, as can be viewed from two angles. First, sleep quality may be viewed from the subjective side, sleeping quality refers to subjective assessment of characteristics of sleep (Krystal & Edinger, 2008). These characteristics include total sleep time (TST), sleep efficiency, feeling hot or cold during the night, sleep onset latency (SOL), total time of being awake during the night, and degree of fragmentation (Krystal & Edinger, 2008). Additionally, quality of sleep may also be measured by occurrence of descriptive events, such as spontaneous arousal or apnea (Krystal & Edinger, 2008). Pittsburgh Sleep Quality Index is one of the most frequently used measures that helps to measure sleeping quality based on these characteristics (Buysse et al., 1989; 1991; 2008; Doi et al., 2000; Smyth et al., 1999). PSQI relies on subjective assessment of the above-mentioned characteristics and events for the period of previous month (Buysse et al., 1989; 1991; 2008; Doi et al., 2000; Smyth et al., 1999). While the method is associated with increased reliability, researchers may view its subjectivity as a possible risk of bias (Buysse et al., 2008). Therefore, developing an objective measure of sleep quality is of extreme importance (Krystal & Edinger, 2008).

Another measure of sleep quality is a collection of objective indices acquired from polysomnography (PSG) in the laboratory (Krystal & Edinger, 2008). The indices may include SOL, TST, wake time after sleep onset, sleep efficiency, and number of awakenings, which corresponds to the characteristics measured PSQI (Buysse et al., 1989; 1991; 2008; Krystal & Edinger, 2008). However, unlike PSQI, PSG provides objective measures of these characteristics using laboratory test and not self-reported test surveys (Buysse et al., 2008; Krystal & Edinger, 2008). In addition, PSG measures how much time a person spends in type 1 sleep (slow wave sleep) and in type 2 sleep (rapid eyes movement sleep). The comparative analysis of PSG and PSQI demonstrated significant differences in observations, which implies that PSQI cannot be used as an equivalent of PSG (Buysse et al., 2008). However, there are significant degrees of correlation between the two measures. Therefore, PSQI may still be used for research if using PSG is impossible for objective reasons (Buysse et al., 2008).

The concept of memory has been bothering the minds of scientists in the course of at least three centuries. The idea that memory is highly dependent upon neurons was known long before the 20^th century (Zlotnik & Vansintjan, 2019). However, the breakthroughs in neurological science of the late 1800s revealed that memory was not associated with the size or the number of neurons (Ramón y Cajal, 1894). In the mid-1900s long-term potential (LTP) was discovered, which led to the understanding that memory is not a strictly neurological process, as chemistry of the brain had a significant impact on the process (Bliss & Lømo, 1973). In particular, the discovery of LTP suggested that memory may be encoded in the strength of the synaptic signals between neurons (Bliss & Lømo, 1973). Since then, the attention to memory as phenomenon increased, which was confirmed by the Nobel prize winning study by Eric Kandel that confirmed the presence of memory even in the simplest organisms in the form of classical conditioning (Kandel et al., 2012). Additionally, research also focused on the chemistry of memory development and recall, which suggested that molecular processes may affect psychological state of people in terms of adaptation (Laferrière et al., 2011). As a result, today, memory is defined as a neuro-chemical process of encoding, storing, and retrieving on information (Zlotnik & Vansintjan, 2019). This study uses this definition of the concept of memory.

Psychologists have learned to distinguish between three different types of memory, including sensory memory, short-term memory, and long-term memory. The names of the types of memories are self-explanatory, as they are names by the key characteristics (Zlotnik & Vansintjan, 2019). Sensory memory is unconscious memory that is closely correlated with classical conditioning theory as an acquired reflex (Alain et al., 1998; Pearson & Brascamp, 2008; Sams et al., 1993; Sligte et al., 2010). Short-term memory can hold only limited information for a limited time period, which implies that retrieval of the memory long-term is often impossible due to the peculiarities in encoding storing of the information (Gathercole, 1999; Hochreiter & Schmidhuber, 1997; Jonides et al., 2008). Long-term memory is characterized by the ability to store indefinite amounts of information for prolonged time periods (Bailey et al., 1996; Hulme et al., 1995; Izquierdo et al., 1999). Thus, speaking of memory, it is crucial to mention the type of memory to which the person is referring. This paper focuses on the concept of short-term memory retention, as memory of the participants is measured by old/new memory test. The test measures how well the participants remember the words they were exposed to during the experiment. Thus, this thesis discusses the effect of sleep and Intelligence on short-term memory.

Current studies of memory focus on the key question of how memory is consolidated and processed (Zlotnik & Vansintjan, 2019). In simple organisms, long-term memory is known to be consolidated on the synaptic level (Bramham & Messaoudi, 2005). However, Frankland and Bontempi (2005) state that memory in more complicated organisms has a second type of consolidation, which is called systems consolidation, which moves, processes, and stores memory permanently. There are two basic models of memory consolidation, including single-system models and multiple-trace theory (Briglia et al., 2018; Hintzman, 1990; Versace et al., 2014; Whittlesea et al., 1987). The single-system models explain that memory hippocampus supports the neocortex in encoding and long-term storing of information (Zlotnik & Vansintjan, 2019). According to the model, the memory becomes free of hippocampus after the strengthening of the connections between neurons (Zlotnik & Vansintjan, 2019). The multiple-trace theory posits that each memory has a memory trace, which continues to involve the hippocampus permanently Briglia et al., 2018; Hintzman, 1990; Versace et al., 2014; Whittlesea et al., 1987). Research revealed significant correlation between memory consolidation and sleep, which will be discussed further in this chapter.

Intelligence quotient (IQ) is a measure of a person’s reasoning ability. IQ is also commonly known for measuring a person intelligence. According to Resing and Drenth (2007), intelligence is “The whole of cognitive or intellectual abilities required to obtain knowledge, and to use that knowledge in a good way to solve problems that have a well described goal and structure” (p. 7). IQ measures how well a person can use available information to make conclusions and predictions (Kush, 2013). The tests for IQ incorporate assessment of several aspects of a person’s intelligence, including short-term and long-term memory (White, 2019). Testing for IQ may also involve asking to solve puzzles and recall information (White, 2019). IQ is commonly known to measure intelligence of students to identify their needs and provide special education (Kush, 2013). The results of IQ are dependent on age; therefore, test results are often compared to the results of the age group (Kush, 2013).

IQ has various aspects; however, it can be measured using vocabulary IQ with a high degree of reliability. For instance, American Psychological Association recommends using Wordsum test as a substitute to the full-scale IQ test, when conducting a full-scale IQ test is impossible for objective reasons (Huang & Hauser, 1998). The test consists of 10 or 20 questions that ask the respondents to find a synonym to words from lists of five choices (Huang & Hauser, 1998). Even though the method is different from the standard IQ tests, it can be used as substitute in research with a sufficient degree of reliability (Huang & Hauser, 1998).

Relationship between Sleep and Memory

When the body is active, it is exposed to various experiences in which it learns gets new information. During sleep, the reactivation and consolidation of the memory occurs where the brain consolidates fragile new memories into permanent forms of long-term storage (Tucker et al., 2020).

The consolidation of memory occurs during the deep stages of sleep commonly rapid eye movement (REM) stage. At this level, related memories are linked together and grouped in unexpected way helping the brain to gather store long-term memory and help in solving complex tasks by organizing the ideas (reasoning).

Procedural memory is also known as implicit memory which help a person to perform a certain task without conscious of the previous actions. It is a long-term memory involved in the performance of various task that are common in one’s life.

Several studies conducted in adults confirm that sleep helps in improving procedural memory and it is depicted in skills and procedures. Since sleep helps in consolidation of memory, when new skill is gained it is stored as a long-term memory hence helping in implicit memory (Cousins et al., 2020).

Procedural memory occurs due to consolidation of memory in deep sleep during the REM stage. It is an essential stage for learning and memory especially when processing newly acquired simple skills considered for long-term. The storage of memory for long-term use confirm that sleep is critical in procedural memory in coming up creative approaches in solving problems.

Other studies have confirmed consolidation of memory in deep sleep where the process begging by assessing sleep’s ability to preserve episodic memories. The long-term memories created in this deep sleep (REM) stage involves the remembrance of the sequence in which acts occurred and stored for future use (Berres & Erdfelder, 2022).

Averagely, human adults need about 6-8 hours of sleep which contribute for about 20-30% of their entire life. Thus, the human body is naturally created to this cycle when recreating its sleeping pattern (Peng et al., 2020). Although there is no exact recommended time to sleep which will ensure that human have a better rest, the resting period is measured in hour to determine if one had adequate sleep. Further, the brain acquires and recalls functions when it is awake and consolidates it when sleeping. Therefore, without adequate sleep, all the body organs would not have rested enough making it harder for the brain to acquire and recall information even when it is awake. Additionally, the consolidation time is also affected which will contribute to loss of memory or creation of a short-term memory.

A recommend sleep should be balanced between hours of sleep and wakefulness. Therefore, a prolonged wakefulness or hours of sleep affect the brain and determine its ability to acquire new information. Acute total sleep deprivation decreases both short-term memory, performance and long-term memory. According to Manassero et al., (2022) even one night sleep deprivation could interfere with the sleeping pattern of human being and affect their brain performance. This shows how sensitive sleep is to the brain and how human should always organize themselves and ensure they have enough sleep. Therefore, having enough sleep will ensure that memories are consolidated especially during the NREM stage where the brain will sort important memory and eliminate others improving the quality of memory.

Every aspect of sleep affects memory including the quality of sleep. Sleep quality is considered as individual satisfaction with the sleeping experience which include the comfort of sleep. Sleep quality includes four attributes which include efficiency, latency, duration and wake after sleep onset. These four factors are further influenced by firstly physiological presentation of the body such as age, body mass and REM. Secondly is psychological factors such as stress and depression which interfere with the min state of an individual. Lastly are the environmental factors such as coldness or warmness of the room (Joo et al., 2021). All these cases should be taken into consideration when determining the quality of sleep that one has received. These factors determine the comfort of sleep leading to satisfaction hence quality sleep. However, these factors determined easily therefore depend on an individual to ensure they get quality sleep by seeking what increases their satisfaction.

Good sleep quality has positive impacts on an individual as it improves their relationship with others by improving their moods, normal flexes and feeling rested. Since the primary goal of sleeping is resting the body and quality sleep ensured it achieved its objective, it depicts the body was able to reach the deep sleep stage where there is consolidation of memory. Additionally, a well-rested body will acquire and recall new information easily which will be considered in consolidation stage creating a long-term memory. With great short-term and long-term memory, an individual will be more effective especially is handling task and solving problem both conscious or unconsciously.

On the other side, poor sleep quality has negative effects to the individual. These consequences include increased irritability, slow response, and daytime dysfunctional and increased intake of caffeine. These side effects indicate poor memory or contribute to poor memory. The intake of caffeine decreases sleep quality further leading to body dysfunctional and further slow response (Joo et al., 2021). When sleep is affected, the consolidation of memory will be affected too thus affecting the acquiring of new information. This affects the overall performance of an individual especially in handling various task. Therefore, sleep quality is essential because it helps in memory consolidation and poor sleep quality will cause health complication to an individual.

Communication in the brain is effective due to coordination between brain neurons. This include communicating to the brain when the body is tired and wants to rest to facilitating memory consolidation. Therefore, to link sleep to memory, one should understand the neurobiology of sleep and how it facilitates memory consolidation. Earlier before the discovery of the role of NREM stage in memory consolidation, Rem was thought to be the only sleep stage that supported memory consolidation (Frick et al., 2020).

After discovering NREM plays part in memory consolidation, various models including Active System Consolidation (ASC). The ASC model explains that long-term memory is aided by the coordination between hippocampal and neocortical system where hippocampal complex from mnemonics is converted into neocortex. The slow oscillation during the NREm sleep, facilitate the reprocessing of memory traces within the neocortex network (Reyes-Resina et al., 2021). Further, sleep reactivates memory traces of the same schemata through slow oscillation activities creating a stronger network.

Relationship between Sleep, Intelligence, and Memory

The inter-relations between sleep and intelligence is an emerging topic in scientific literature. While the correlation between two concepts is a matter of interest, the literature on the subject is limited. There is abundant evidence that sleep deprivation has a significant impact on intellectual abilities of people (Fogel et al., 2011; Killgore et al., 2008; Nowack, 2017). Lack of sleep leads to decreased amount of sleep spindle, which is closely correlated with the IQ tests (Fogel et al., 2011). Sleep deprivation leads to daytime sleepiness, which negatively affects the ability to concentrate on one object or phenomenon (Nowack, 2017). As a result, a person’s ability to make logic decisions decreases, which naturally reduces a person’s IQ test scores (Nowack, 2017). In workers, sleep deprivation leads to decreased decision-making abilities, increased absenteeism, and increased number of conflicts (Nowack, 2017). Additionally, inadequate sleeping patterns may lead to significant problems with social functioning and decreased ability to perform duties (Nowack, 2017). Killgore et al. (2008) state that decreased cognitive ability in people deprived of adequate sleep is associated with decreased adaptive functioning. As a result, emotional intelligence (EQ) of people decreased, leading to lower scores of IQ and EQ tests (Killgore et al., 2008). However, the effect of sleep on IQ becomes evident only when people demonstrate significant sleeping problems, such as lack of sleep.

It should be noted that there is a two-way relationship between sleep and IQ. According to Ujma et al. (2020), intelligence may have a significant effect on sleep quality. In particular, intelligence may cause individual differences in sleeping patterns and individual characteristics of sleep (Ujma et al., 2020). However, despite the large body of literature concerning the relationship between IQ and sleep, the correlation between the two concepts is very small, but significant (Ujma et al., 2020).

The body of published literature concerning the relationship between IQ and memory is emerging. There are emerging theories that intelligence influence the patterns of how children learn to remember things (Colom et al., 2010). Additionally, there exists emerging empirical evidence that there is a correlation between working memory, secondary memory, and general intelligence (Shelton et al., 2010). Additionally, tests for IQ measure both short-term and long-term memory, which creates theoretical basis for assuming that there is a positive correlation between IQ and memory retention (White, 2019). Thus, even though the evidence on the correlation between memory and IQ is slim, there is enough basis for Hypothesis 3 mentioned in Chapter 1 of this thesis.

Theoretical Frameworks

Neurobiology has taken many paths in the past years with many scientists especially clinicians and psychologists. These individuals have studies the science of memory creation and recalling it when required. Thus, they give trace of how the memory was created, stored, and reactivated or triggered. Through these studies, memory has been perceived as an open-ended process while retrieval identified as a complex and primary part of encoding process to confirm the existence of the long-term memory (Giri et al., 2018).

Despite scientist proving that consolidation helps stabilizing of memory for long-term in human brain, other researchers believe that the new learning is fragile and could be affected by a number of factors that happen before one goes to sleep. This theory depicts that the newly acquired information will further be affected by the subsequent events that will determine its consolidation during sleep. The subsequent events involve even those that occurs during the early sleep stage when the body has not lost sensory to the surrounding.

The active systems consolidation hypothesis explain that the synapses involved in early stages of sleep learning and memory increases their effectiveness and are consolidated during the sleep. This approach depicts that the body is responsive to the environment until the pre-sleep stage and all this information is consolidated when one reaches deep sleep stage (Quillfeldt, 2019). However, this memory remains unstable until stabilized during consolidation. Therefore, the sorting of information is done depending on its importance and subsequent actions which will create a relationship and help in tracing in the long-term memory. In many contexts, system consolidation may take longer in some individuals such as those suffering from medial temporal lobes (MTL). The MTL patients recent memory is of greater importance than earlier memories that occurred before amnesia, thus they will consolidate the most recent memories.

Human beings are able to adapt to different environment and acquire new information. The synaptic homeostasis hypothesis (SHY), suggests that sleep is the ultimate price brain pays for its adaptability to changing environment. When a person is a wake, he requires the brain coordination to learn thus brain forced to adapt to new experiences continuously (He & Hu, 2017). When the reprogrammed body goes to rest with the help of the neurons coordination, other parts rest but the brain and other organs remain functional although in lowered metabolism. The brain is forced to process the temporary new acquired information to permanent memory thus a price it pays for its plasticity.

Research Design and Justification

The purpose of this research was to assess the relationship between sleep quality, sleep quantity, IQ, and memory. Therefore, the most appropriate design for this research was qualitative correlational design, as it allowed to achieve the purpose of the study. The are three general approaches to research is psychology, including qualitative, quantitative, and mixed-method research (Cuttler et al., 2019). Qualitative methods are usually used to explore a problem or a phenomenon in a broad sense by assessing thoughts and experiences of people concerning the matter (Creswell, 2007). Data collection methods associated with the qualitative methods are interviews, case studies, focus groups, and observations (Creswell, 2007). Data analysis methods associated with the qualitative approach are thematic analysis, grounded theory, narrative, discourse, and qualitative content analysis (Creswell, 2007). The central benefits of the approach are its flexibility due to a decreased number of procedural limitations and cost-effectiveness (Creswell, 2012). At the same time, qualitative research helps to gain detailed in-depth understanding of a phenomenon (Creswell, 2012). However, the method is associated with significant bias due to increased subjectivity (Cuttler et al., 2019). Moreover, qualitative methods are associated with decreased breadth of received knowledge (Cuttler et al., 2019). This approach was considered inappropriate for this study, as the phenomena of sleep, IQ, and memory are well studied and qualitative methods are inappropriate for the

The qualitative approach is the opposite of the qualitative design, as it focuses on the breadth of the knowledge rather than depth (Creswell, 1994). Qualitative research uses rigorous data collection methods and statistical analysis to test hypotheses (Heath, 2018). The central benefit of quantitative research is revealing very specific knowledge with minimal bias (Heath, 2018). Quantitative research relies on surveys and observations to collect the data and statistical models to test hypotheses (Creswell, 1994). This research aimed at testing three hypotheses listed in Chapter 1; therefore, quantitative approach was considered the most appropriate to achieve the purpose of this study. A mixed-method approach was also considered for this paper, as it can help to negate the drawbacks of the qualitative and quantitative approach (Cooper & Schindler, 2014). However, using a mixed method may also be associated with increased risk of error and bias (Cooper & Schindler, 2014). Therefore, due to the limited experience of the researcher, it was decided against using a mixed-method approach to achieve the goal of the study.

Correlational research design is used when there is a necessity to explore the relationship between two or more variables (Cuttler et al., 2019). Correlation studies are non-experimental, as they measure and analyze the variables without manipulating any of them (Cuttler et al., 2019). The purpose of the correlational research is to quantify the strength of relationship between variables without the ability to measure the cause-and-effect relationship (Heath, 2018). Correlational studies have limited control over the environment; however, its external validity is high due to increased generalizability in comparison with experimental studies (Cuttler et al., 2019). Since the purpose of the paper was to study the relationship between four variables, correlational design appears the most appropriate.

Method

Participants

Population

The population under analysis is the students of the University of Sheffield. According to the official website of the university, total population is 30,129 (University of Sheffield, 2022). The inclusion criterium for the study was to be the student of the current University of Sheffield. The exclusion criterium was being below 18 years of age.

Sampling Method

There are two groups of the sampling methods, including probability and non-probability methods (Elliott & Valliant, 2017). Probability methods include four subtypes, including simple random sampling, systematic random sampling, stratified sampling, and cluster sampling (Cuttler et al., 2019). Probability sampling methods allow every member of the population have similar chances to become a part of research (Etikan & Bala, 2017). Probability sampling methods are used when participant selection bias is a significant concern (Etikan & Bala, 2017). Non-probability sampling methods are subdivided into convenience, purposive, snowball, and quota sampling (Elliott & Valliant, 2017). Non-probability sampling methods are used when there is low chance of sampling bias and finding the target audience is simple (Cooper & Schindler, 2014). Even though the selection bias was not a significant concern for the study, it was decided to favor probability sampling methods, as they help to reduce bias. Moreover, the University of Sheffield provided the research with the opportunity to use its participant volunteer mailing list, which is can be used for random sampling.

The selected sampling method is simple random sampling due to its ease of used. According to Etikan and Bala (2017), simple random sampling method requires minimal skill to be executed, which was crucial due to the limited experience of the researcher. This method usually presupposes that a list of participants is created and randomization software is used to select the needed number of participants. The total number of needed participants was estimated to be 100 with the help of the expert opinion of the supervisors.

Recruitment Procedure

The participants were recruited using the University of Sheffield’s participant volunteer mailing list and social media. The researcher received an authorization to advertise the research using the participant volunteer mailing, which helped the researcher recruit 63 participants. However, since the number was not enough, the research used social media to recruit the participants. In particular, an advertisement was created on the official Facebook page of the university to ask the students to join the study. The researcher also sent the direct invitations to potential participants using social media. The researcher randomly selected students of Sheffield University and sent them invitation emails using the internal messaging system of Facebook.

A total of two emails were sent to each participant. The first letter was included information on the purpose of the study and the manipulations the participants were expected to do. The invitation letter also stated that there would be no repercussions associated with the rejecting the offer to participate in the study. Additionally, potential participants were informed that they could withdraw from the study anytime before completing the questionnaires. However, excluding the data acquired from the participants after the participant submitted the results would be impossible due to the inability to identify the data by participant. The recruitment letter also included the informed consent form, and the participants were asked to read carefully and confirm their consent with terms of the study. If the participants replied with agreement to participate in the study and confirmed their consent, they were sent the link to the online questionnaires along with the instructions for the questionnaires.

Materials

The study focused on the correlation between sleeping quality, IQ, and memory. The concept of sleeping quality was subdivided into two variables, including ‘sleeping quality’ and ‘sleeping quantity’. The sleeping quality variable is subdivided into two aspects, including perceived sleeping quality and sleeping problems. Additionally, demographic variables were measured, including gender, age, and education level.

A total of four instruments were used to measure the variables. First, a self-created questionnaire was used to assess the participants’ gender, age, and education level. The gender variable was a categorical variable, which had two categories, including ‘male’ and ‘female’. The participants’ age was a continuous variable, which was measured by asking the participants to state the number of full years. Education was a categorical variable that included five options, including lower than high school, high school, undergraduate degree, and postgraduate degree.

Second, the participants’ memory was tested using old/new recognition task, which consisted of two blocks. During the first block, the participants were asked to memorize a list of 15 words. During the second block, the participants were provided with a set of words and asked to determine if they were included in the original 15 words. The number of correct answers to the task was used as the measurement for the variable. According to Rugg and Wilding (2000), old/new recognition test is a reliable method for retrieval processing and episodic memory.

Third, Pittsburgh sleep quality index (PSQI) questionnaire was used to measure the sleep quality of the participants. PSQI has several dimensions measured using ten blocks of question (Buysse et al., 1989). Three dimensions of PSQI were used to measure the sleeping quality, including sleeping quantity, perceived sleeping quality, and intensity of sleeping problems. Sleeping quantity was measured using Question 4 of PSQI, which asked the participants to estimate the average number of hours participants slept every day. Sleeping problems was measured as a sum of scores for assessing all the possible problems, including waking up during the night, inability to go to sleep within 30 minutes, having to go to the bathroom during the night, coughing, snoring, feeling cold, feeling hot, and having bad dreams. The perceived quality of sleep was measured using Question 6 of PSQI, which asked the participants to assess on a four-point Likert scale the quality of their sleep during past month. The dimension of sleeping problems and perceived sleeping quality were summed to create the variable of sleeping quality.

Fourth, the Wordsum test was used to measure the IQ variable. Wordsum is a 10-item vocabulary test used in General Society Survey to measure the intellectual quotient (IQ) of the participants. According to Siegel (2017), the Wordsum test is a valid and reliable tool that can be used to measure the IQ of the general population. During the test, the participants were asked to find synonyms to the offered word out of five alternatives. The number of correct answers to the Wordsum test was used as the measure for the person’s IQ by this study.

Procedure

Data Collection Procedure

This study relied on primary data rather than using secondary data. The use of secondary data is becoming increasingly popular in medical and sociological research, as it is associated with significant benefits (Cole & Trinh, 2017). In particular, secondary data analysis is associated with increased reliability and validity of measurements, as the data s usually collected by highly qualified professionals (Cuttler et al., 2019). Moreover, the use of secondary data is associated with increased cost and time efficiency, which allows to produce evidence at a higher speed (Heath, 2018). However, despite the evident benefits of secondary data utilization, it was decided against using the secondary data for this research. The primary reason for avoiding secondary data was the inability to find relevant datasets to answer the research questions.

Primary data was collected using only survey applications that help to automate the data collection process. Gorilla is commonly used tool by students and researchers for online experiments (Gorilla, n.d.). Unlike other survey tools, such as Survey Monkey and Google Forms, Gorilla provides tools for more complicated tasks, which are unavailable in other survey services. Gorilla software allowed to conduct the old/new memory test, which would be impossible to conduct using survey tools.

The data was collected automatically by providing the link to the recruited participants via email. The participants were provided four links to four different surveys. The total number of questions was 57. The average time spent on answering all the questions was 15 minutes. The participants could reply to all four surveys during one session or several sessions. Since all the participants were provided a unique internal identifier, the data could be compiled together even if the participants chose to reply to the questions over an extended time period. The participants were asked to follow all the instructions included in the description of every task to ensure that they understand everything correctly. After the data was collected, it was automatically stored in the Gorilla cloud protected by the Gorilla’s data security tools.

Data Collection Procedure

There are two central methods utilized in correlational studies, including Pearson’s correlation coefficient and regression analysis (Creswell, 1994). Pearson’ correlation analysis is the basic method utilized to determine the magnitude of correlation between two variables. The coefficient ranges between -1 and 1, with 1 standing for perfect positive correlation, -1 standing for the perfect negative correlation, and 0 standing for no correlation (Cuttler et al., 2019). While this method is appropriate for determining the level of correlation between two variables, it is inappropriate for determining a combined effect of several independent variables on one dependent variable (Heath, 2018). Thus, it was decided to use multiple regression analysis to determine the effect of sleeping quality and IQ on memory. Additionally, the effect of variables was also control with demographic variables, including age and gender.

According to Creswell (1994), multiple linear regression is the most common method for quantifying the effect of several independent variables on one dependent variable, which suits the purpose of this study. There are other methods that may be appropriate for quantifying the relationships between several variables, including quadratic regression, multilevel analysis, logistic regression, and ridge regression. However, due to the limited experience of the researcher, it was decided to use multiple linear regression to create a basic quantitative model for quantifying the relationships between the variables mentioned above.

Two models were assessed to determine the effect of sleeping quality, sleeping quantity, and IQ on memory. The first model did not control for gender and age, while the second model controlled for gender and age of the participants. The models are provided below:

Model 1:

Model 2:

The data was analyzed using Microsoft Excel 2019 and Statistical Package for Social Sciences (SPSS) Version 26. First, the raw data was downloaded from Gorilla in four separate datasets for every questionnaire and formatted using pivot table function in Excel. Second, four datasets were compiled into one dataset for further analysis on the basis of internal ID of the participants. Third, the data was checked for incomplete questionnaires to exclude from analysis. No incomplete surveys were found. Finally, the data was imported to SPSS and two regression models were analyzed using multiple linear regression.

Ethical Considerations

The study was conducted in accord with the regulations of the institutional ethics committee. All the participants were sent the informed consent form and asked confirm their agreement with the terms of the research. The participants were informed that they could withdraw from the study at any moment before submitting the final questionnaire and their replies would not be included in the analysis of data. No financial incentives were provided to the participants to avoid ethical problems, such as undue inducement, exploitation, and biased enrollment. The research was advertised using approved channels, including the volunteer list and official channels.

There was no perceived physical hard or psychological distress to the participants during the research. The surveys were completed online at the participants’ convenience. The participants were informed that no personal data was collected and all other information was protected in accordance to the highest standards. If the participants felt any inconvenience they could withdraw from the study without any legal, moral, or financial consequences. The participants were also directed to report their concerns to the supervisor within the information sheet of the designated safeguard contract as well as the University safeguard policy.

Gorilla Software that was used to collect the required data met the required guidance in terms of data protection and confidentiality. Participants were given specific identification numbers, which helped to avoid using their name or any other personal information to ensure confidentiality. Moreover, all the information was stored in the Gorilla software platform servers and University’s secure Google drive, which allowed access only of the authorized personnel. Only the researcher and the supervisor had access to the collected information, which ensured security of data storage. The participants were allowed to receive only the summary of findings, and they had no direct access to the data. All the data was scheduled to be destroyed on September 6, 2022, after the completion of the study.

Results

Sample Description

The final sample included 100 students from the University of Sheffield. Since the number of 100 participants was the suggested number of respondents by the supervisor, the recruitment process stopped as soon as the suggested number of participants was achieved. All the participants completed their surveys in full, and the age of all the participants was above 18. Therefore, no responses were excluded from the analysis. The final sample included 55 females, 40 males, and 5 participants preferred not to mention their gender.

The ages of the participants ranged between 18 and 47, with 89% of the participants being 30 or younger. The mean age of the participants was 24.83 with a standard deviation of 4.92. The distribution of gender was not close to the normal distribution curve, as it was extremely leptokurtic (kurtosis = 3.97) and positively skewed (skewness = 1.48). The age distribution of the sample is visualized in Figure 1 below.

The majority of the participants had either an undergraduate degree of a post-graduate degree. In particular, eight participants stated that they had high school education, 47 participants reported to have an undergraduate degree, 41 participants reported to have a post-graduate degree, and four participants preferred not to say about their education level.

Descriptive Statistics of Variables

This section focuses on the discussion of the descriptive statistics of the variables. According to McClaive et al. (2018), descriptive statistics serve two crucial roles in analysis of data. First, descriptive provide a bird’s eye view of the data using the measures of central tendency and dispersion, which allows the reader to understand how the data was distributed (McClaive et al., 2018). Second, descriptive statistics may be helpful for determining correlation between variables (McClaive et al., 2018). Apart from these two benefits of descriptive statistics, such analysis is often used to determine the distribution pattern of variables to determine if it is in accord with the assumption of normality. According to McClaive et al. (2018), normality of distribution is a crucial assumption for multiple regression analysis. This research analyzed the variables in terms of mean, median, mode, standard deviation, maximum, minimum, kurtosis, and skewness. The summary of the descriptive statistics is provided in Table 1 below.

Table 1. Descriptive statistics of variables

Sleep Quantity

Sleep quantity was measured by asking the participants to estimate an average number of hours of sleep they had a day during the past week. The mean value for variable was 7.69 with the median and the mode being equal to eight. The standard deviation was 2.107, with skewness of 1.471 and kurtosis of 14.998. The descriptive statistics revealed two potential problems with the measurement of the variable. First, the distribution appeared extremely leptokurtic, as the majority of replies clustered between six and nine hours of sleep. This demonstrates that the distribution does not follow the assumption of normality. Second, the minimum value was 0 and the maximum value was 20, which appears unrealistic for the average duration of sleep during the week. Thus, descriptive statistics revealed that at least four of the surveys provided biased data, as the average number of hours of sleep appeared unrealistic, as can be seen in the frequency table provided in Figure 2 below.

Sleep Quality

The quality of sleep was measured by adding the scores of sleeping problems and perceived quality of sleep. The mean score was 18.78, with the median score of 18.5 and mode of 10. The standard deviation was 5.904 with kurtosis of -0.48 and skewness of 0.364. The analysis of descriptive statistics demonstrated that the distribution of the sleeping quality was close to normal distribution, as both kurtosis and skewness were close to zero. The distribution of the data revealed no outliers that may have affected the analysis.

Memory

The participants’ memory retention was measure by the sum of correct answers to the old/new memory test. The total number of questions was 20, which implies that the values had the potential to vary between 0 and 20. The mean value for the memory test was 8.45, with the median of 8.0 and the mode of 6.0 and 7.0. The standard deviation was 4.59 with skewness of 0.377 and kurtosis of -0.609. The sum of correct answers to the Wordsum test varied between 0 and 19. The distribution of the values was close to the normal distribution, as both skewness and kurtosis were close to zero. No significant outliers that could have affected the results of study were found.

Intellectual Quotient (IQ)

The IQ of the participants was measured by the number of correct replies to the 10-item Wordsum test. Since the test had 10 questions, the observations varied between 0 and 10 for the variable. The mean value was 5.26 with the median of 5 and two modes of 3 and 8. The standard deviation was 2.473 with kurtosis of -1.186 and skewness of -0.015. The kurtosis of the distribution demonstrates that the assumption of normality was violated. Moreover, the fact that the distribution has two modes far away from each other demonstrates that it has two peaks, meaning the the distribution is not bell-shaped, as demonstrated in Figure 3 below.

Hypotheses Testing

A total of two models were assessed to test the three hypotheses mentioned in Chapter 1 of this paper. The first model included only dependent and independent variables, while the second model included dependent, independent, and control variables, which were gender and age. Model summaries are provided in Table 2 below.

Table 2. Model summaries

The coefficient of determination of the first model was very low with R² = 0.089. This implies that the variations in the independent variables could explain only 8.9% of variations in the independent variable. Even though the coefficient of determination of the second model increased to R² = 0.09, the increase may be due to the increased number of variables. Thus, the model had very low level of predictive ability.

The analysis of coefficients demonstrated that only sleep quantity was a significant predictor of memory retention with p = 0.018 in the first model and p = 0.022 in the second model. Sleep quality was an insignificant predictor with p = 0.071 in the first model and p = 0.068 in the second model. It should be noticed that even though sleep quality was close to being statistically significant, the p-values in both models were slightly above the threshold of alpha = 0.05. IQ was not a significant predictor for memory retention with p = 0.716 in the first model and p = 0.667 in the second model. The control variables were not significant in the second model with p-value being 0.749 for age and 0.866 for gender.

The analysis of the results demonstrated that Hypothesis 1 should be accepted, as there is a significant positive correlation between sleep quantity and memory retention. In other words, the more hours on average a person sleeps during a week, the higher their score on the old/new memory test.

Hypothesis 2 was rejected, as the p-value for the sleep quality was below the threshold of alpha = 0.05, which implies that there was no significant correlation between sleep quality and memory retention. In other words, the changes in the sleeping quality did not affect the scores in the old/new memory test among students of the University of Sheffield.

Hypothesis 3 was also rejected, as the value for IQ was below the threshold of alpha = 0.05. This implies that that there was no significant correlation between the participants’ IQ and memory retention. In other words, the participants’ IQ did not affect their scores on the old/new memory test.

Discussion of Assumptions

There are several assumptions of the regression model that should be tested before accepting the results. The first assumption of regression analysis is normality of the variables, which means that all variables should be normally distributed (Osborne & Waters, 2002). The discussion of this assumption was included in the analysis of the descriptive statistics. The analysis revealed that two of the variables were not normally distributed, including sleep quantity and IQ. As a result, the analysis of correlations between the variables using multiple linear regression may be associated with significant biases (Berry, 1993). However, Williams et al. (2013) stated that the “multiple regression models estimated using ordinary least squares require the assumption of normally distributed errors in order for trustworthy inferences, at least in small samples, but not the assumption of normally distributed response or predictor variables” (p. 2). The analysis of histogram of the errors provided in Figure 4 below demonstrated that they normally distributed around the regression line. Therefore, according to Williams et al. (2013), the model fulfills the assumption of normality.

Another assumption of the regression analysis is linearity, which implies that the correlation between the dependent variables and the independent variables should be linear (Watt & Collins, 2019). There are several ways to establish linearity among variables. One of the simplest ways is to rely on previous research that has established such a type of relationships between two variables (Osborne & Waters, 2002). However, it is not the most reliable way, as it may be challenging to find research that considered non-linear relationship between the variables. The second method for testing for non-linear relationships is to create scatterplots for all the variables and eyeballing the correlation between the variables (SPSS Tutorials, n.d.). Even though such analysis was conducted, it was difficult to spot the pattern f correlation between the variables. Thus, the third method was employed to check for linearity suggested by Osborne and Walters (2002). The methods suggested that the residual plot should be inspected to analyze its pattern. The analysis of the plot provided in Figure 5 below demonstrated that the models did not violate the assumption of linearity.

The third assumption is absence of multicollinearity is also crucial for regression analysis. This assumption suggests that there should not be high correlation between the independent variables to ensure that the regression model is not biased (Mason et al., 1991). Multicollinearity may be tested by created a Pearson’s correlation matrix and inspecting the correlation coefficients between the variables (Næs & Mevik, 2001). Any correlations above the magnitude of 0.8 may be a significant problem for the reliability of the regression analysis (Berry, 1993). The correlation matrix provided in Table 4 below demonstrates that there were no strong correlations between the independent variables. Thus, it was decided that the assumption of multicollinearity was not violated.

Table 4. Correlation matrix C

The fourth assumption of the regression analysis mentioned by Osborne and Walters (2002) is reliability of measurements. In other words, the regression analysis requires that the instruments utilized for measuring the variables had high reliability. The reliability of the PSQI was assessed using Chronbach’s alpha, which was 0.801. This is an acceptable level of reliability of the instrument. Other instruments, including Wordsum test and old/new memory tests were developed by professionals to measure IQ and memory retention correspondingly. Therefore, it was decided that the assumption of reliability was not violated in the model.

Discussion

Explanation of Results

Hypothesis 1

The test for Hypothesis 1 demonstrated that there was a significant correlation between the average sleep time and number of correct answers to the old/new memory test in students of the University of Sheffield. In other words, the model provided significant evidence that there is a positive correlation between the quantity of sleep and memory retention in the target population. However, Pearson’s correlation analysis demonstrated that the magnitude of the relationship between sleep and the average hours of sleep is small with r = 0.234. The coefficient of determination of the regression model demonstrated low predictive ability of the model, which also confirms that correlation between the variables was weak.

The findings are consistent with the results of previous research that state that there is a positive correlation between the quality of sleep and memory (Joo et al., 2021). Since the total time of sleep is one of the components of sleep quality according to both objective and subjective measure of sleep quality, the results of this research do not contradict previous findings (Buysse et al., 1989; 1991; 2008; Doi et al., 2000; Krystal & Edinger, 2008; Smyth et al., 1999). However, the relationship was weak because the majority of participants slept for 7-9 hours a day, which is acceptable duration of sleep for adults between 18 and 64, according to the American Sleep Foundation (Suni, 2022). Significant problems with memory emerge in cases of sleep deprivation (Peng et al., 2020; Manassero et al., 2022). The sample included 12 students who slept 6 hours a day on average, two students who slept five hours a day of average, and two participants that reported to have an average of 0 hours of sleep a day. Assuming that the two participants who reported having no sleep for the past month misunderstood the task, there are only two people that may have had cases of sleep deprivation since they slept only for five hours a day for a month.

Another reason for the weak correlation between the variables may be the fact that the old/new memory test measured short-term memory rather than long-term memory. According to Tucker et al. (2020), the central reason for the correlation between memory and sleep is the process of memory consolidation. During sleep, the reactivation and consolidation of the memory occurs where the brain consolidates fragile new memories into permanent forms of long-term storage since consolidation of memory occurs during the deep stages of sleep commonly known as rapid eye movement (REM) stage (Tucker et al., 2020). The participants did not have time to sleep during the experiment. Therefore, this research discovered correlation between the average total sleep time and the ability of students to use short-term memory.

Hypothesis 2

The test for Hypothesis 2 revealed that there was no significant correlation between the quality of sleep and memory retention. In other words, the results of model assessment demonstrated that the students’ sleep quality measured subjectively had no significant effect on their old/new memory test results. However, both regression models and Pearson’s correlation analysis demonstrated that the relationship between sleep quality and memory test scores almost reached statistical significance with p-value ranging between 0.065 and 0.071.

The results of the research are inconsistent with previous findings, which stated that there was a positive correlation between memory sleep quality (Joo et al., 2021). However, as it was mentioned previously in this chapter, previous research focused primarily on the correlation between sleep quality and long-term memory (Born, 2010; Tucker et al., 2020). Since this research focused on the correlation between sleep quality and short-term memory, the study addressed the important gap literature concerning. Even though there is emerging evidence that sleep quality may affect short-term memory in mice (Tam et al., 2021), no research concerning the correlation between short-term memory and sleep quality in people was found during the literature review.

There may be several reasons for negative results of the analysis of correlation. First, it may be concluded that sleep quality does not affect short-term memory in all populations. However, since the results of this research are specific to the students of the University of Sheffield. The analysis of descriptive statistics demonstrates that the students of Sheffield University had relatively high quality of sleep quality with the mean value of 18.78 and standard deviation of 5.9. Since the PSQI testis based on a reversed four-point Likert scale, the maximum value of 40 stand for the worst possible sleep quality, and the minimum value of 10 stand for the best possible sleep quality. This demonstrates that the mean value was lower than the natural mid-point of 25, which may indicate high quality of sleep among the participants. Thus, average quality of sleep may have been too high to have a significant effect of short-term memory. In other words, the results of this research may be not generalizable to the general population.

Third, the research may be affected by confounding variables. For instance, the effect of the quality of last nights’ sleep may have had a higher effect on short-term memory rather than the average quality of sleep during the past month. In other words, memory test results may be biased, as the test was taken only once. Therefore, since the study lacked control for confounding variables, the results may be inaccurate. In summary, despite the insignificant correlation between sleep quality and short-term memory revealed in this study, the study demonstrated that there is basis for further research on the matter to improve the reliability and sensitivity of data collection methods.

Hypothesis 3

The results the study demonstrated that Hypothesis 3 should be rejected. In other words, there is no significant correlation between people’s IQ and their short-term memory. In other words, the participants’ score on Wordsum test had no significant effect on their old/new memory test results. The p-values of the regression analysis demonstrated that the correlation between the variables was far from being significant, ranging between 0.67 and 0.81 in the regression models and Pearson’s correlation analysis.

Previous research on the subject was scarce, which makes this study unique. Ujma et al. (2020) stated that there is inter-dependence between person’s intelligence and their memorizing patterns, which may affect both short-term and long-term memory. Even though previous studies mentioned that there is theoretical basis for the relationship between memory and intelligence, there were no empirical studies that confirmed the correlation (Colom et al., 2010; Shelton et al., 2010; White, 2019). This study did not confirm the hypothesis using a sample of students from the University of Sheffield. However, the results for other populations may be different, as the population used in this research is comparatively homogeneous in terms age and education level, which may signal about the relative similarity in the level of intelligence.

It should be noted that this research used 10-item Wordsum test for measuring IQ of the participants. The test was used to due to its convenience for the researcher and the participants. However, the test may have had low sensitivity to the actual IQ of the participants. There are other measures of IQ that may have been used to improve the sensitivity of measurements, such as the 20-item Wordsum test or a full-scale IQ test.

Further Considerations

This research provided controls for age and gender, which were both found to have insignificant effect on the memory test scores with p-values of 0.749 and 0.866 correspondingly. This implies that neither gender nor age had a significant effect on short-term memory in students of the University of Sheffield. While the proportion of male participants was somewhat close to the proportion of female participants, there are several biases that should be acknowledged in terms of age. In particular, the 89% of the sample were younger than 30, which implies that the vast majority of participants belonged to one age group. Previous research demonstrates that age may have a significant effect on short-term memory (Anders et al., 1972; Dobbs & Rule, 1989). However, recent research by Yassuda et al. (2020) stated that age does not affect short-term memory retrieval. Instead, the age increases the chance of brain related conditions, which may have a significant effect on both short-term and long-term memory. However, healthy aging is associated with no impairment of short-term memory. Thus, the results of this study in terms of the effect of age on short-term memory are consistent with previous research.

Limitations

In order to understand the applicability of the results of this research, it is crucial to determine the limitations of this study. Acknowledgement of limitations provided in this section aims at putting the research findings in context so that the reader could establish the validity of the scientific work and the credibility of findings. This section provides a list of limitations and a concise analysis of the possible impact on the reliability research findings.

First, the results of this study are limited to the population under analysis. The population under analysis is the students of the University of Sheffield. The inclusion criteria for the study was to be the student of the current University of Sheffield. The exclusion criteria was being below 18 years of age. The population has several distinct peculiarities that make it different from the general population. In particular, 89% of the participants were younger than 30 years but older than 18, which was the inclusion criterion for the study. However, only 22% of the US population belong to the same age group (USA Facts, 2022). Moreover, all of the participants are university students and have a higher than average education level. However, only 61.28% of the population 18 had some college in the US (USA Facts, 2022). Therefore, the population of Sheffield University is a special population. This implies that the results of this research have limited generalizability.

Second, the results of the research are limited by the sample size. According to the sample size calculator provided by Survey Monkey (n.d.), in a population of 30,000 people, a sample of 380 participants is required to have a margin of error of 5%. This study recruited only 100 participants, which implies that there is a 10% margin of error for this study (Survey Monkey, n.d.). This research recruited only 100 participants due to associated difficulties with securing a higher number of participants. In other words, the sample size was selected in the researcher’s convenience after the supervisor’s approval. However, a larger sample may be needed to improve the reliability of findings by reducing the margin of error.

Third, this research cannot guarantee that the sampling was truly random due to the extensive use of the University of Sheffield’s participant volunteer mailing list. In other words, the participants were not randomly selected from students of the University of Sheffield. Instead, they were randomly selected from the university’s volunteer list. It is possible that volunteers may have distinct features that differentiate them from other students of the University of Sheffield. Such characteristics were not accounted for if they exist due to the lack of research on the matter. The lack of certainty in terms of the sampling method being truly random may have a negative effect on the reliability of findings.

Fourth, the assumption of normality was violated for two variables, including IQ and sleep quantity. Regression and correlation analysis require that all the variables are normally distributed for the results to be reliable (Osborne & Waters, 2002). Therefore, the results of statistical analysis may be biased, which may have negatively affected reliability of findings. However, according to Williams et al. (2013), only the errors of the regression model should be normally distributed, which was followed in this research. Thus, the possibility of bias associated with violation of the assumption of normality is minimal.

Fifth, the created model controlled only for two control variables, including age and gender. However, there may be other cofounding variable that affected the reliability of findings, including last night’s quality of sleep, mental conditions, and recent substance use. The participants were not tested for any conditions, such as stress, anxiety, and depression. Moreover, the participants were not instructed to avoid taking the memory test and Wordsum test if they had problems sleeping the night before or used any substances that may have affected their mental state. Therefore, there is a high possibility of confounding variables that affected the relationships between the independent variables and the dependent variable.

Sixth, it is crucial to note this study focused on the effect of IQ and sleep quality on short-term memory after controlling for gender and age. The effects of these variables on long-term or sensory memories was no measured by the model. This implies that the results of the analysis cannot be applied to long-term or sensory memory of students of the University of Sheffield.

Finally, the study is limited by the qualifications of the researcher. Even though the researcher has significant academic background, it is the first large-scale study conducted by the author. The researcher acknowledged this limitation and used every opportunity to consult with University authorities to minimize errors and biases implied by the lack of experience in academic research. However, it may still be a significant limitation to the study.

Recommendations for Future Research

Future research should address the limitations of this study to acquire a deeper understanding of the relationship between memory, sleep, and IQ. The list of recommendations is provided below.

• Conduct similar research in other contexts. The results of this study can be applied only to students of the University of Sheffield, which limits the generalizability of the results. Therefore, it is recommended to study other populations to increase the generalizability of findings. While conducting similar research in the context of university will increase the generalizability of findings, it will not address the limitation of age and education level distribution. Therefore, it may be appropriate to run similar research using a sample from general population or special populations with a more diverse education level and age distributions.
• Use improved sampling techniques. The analysis of limitations acknowledged two problems with sampling. On the one hand, the sample size was inadequate to provide a narrow margin of error. Therefore, future research is recommended to increase the sample size to at least 360 participants to ensure a margin of error of 5%. On the other hand, this research could not guarantee true random sampling, which implies that not all the member of population had a similar chance to be a part of the study. Therefore, future research should improve the sampling techniques to decrease the possibility of sampling bias.
• Address the possibility of interaction with confounding variables. The measurements of the variables may be associated with decreased reliability due to flaws in the data collection procedures. In particular, there is a possibility that measurements may have been affected by the quality of last night’s sleep or substance use, as the study did not control for the possibility of such issues. Therefore, future research should address the possibility of measurement bias. In particular, it may be appropriate to take several measurements of short-term memory to decrease sensitivity to the possible confounding variables. Additionally, it may be appropriate to instruct the participants not to take any tests if their cognitive ability or emotional state was impaired by stress, depression, lack of sleep, or substance use.
• Study the effect of sleep and IQ on long-term memory. This study focused on the effect of IQ and sleep on short-term memory. Previous research did not address the relationship of IQ on long-term memory. Moreover, the literature review revealed a lack of recent evidence of correlation between long-term memory and sleep. Therefore, it may be beneficial to develop research design that will close this gap in the current body of knowledge.

Implications of the Research

This study addressed an important gap in the current body of knowledge by examining the correlations memory, sleep quality, and IQ. Previous research focused on the effect of sleep quality on long-term memory and consistently found significant correlation between the two variables. However, no previous research focused on quantitative analysis of sleep quality on short-term memory. Even though there were some mentions about the correlation between sleep and short-term memory, no empirical evidence was found during the review of literature. Therefore, this study provided unique insights in the correlation between sleep quality and short-term memory, which addressed the gap in the current body of knowledge.

This study is also unique as it separated the effect of the quality and quantity of sleep on short-term memory. Previous research utilized PSQI to measure sleep quality, which included total hours of sleep time. This research separated the concepts fo quality of sleep and quantity of sleep, which generated peculiar results. While sleep quantity had a significant impact on short-term memory, the quality of sleep had no significant effect on short memory.

This thesis addressed the gap in knowledge concerning the effect of IQ on short-term memory. Before this study, there was only theoretical basis for assuming that there is a positive correlation between short-term memory and IQ. This study measured the correlation using rigorous quantitative research methods. Even though no significant correlation between the variables was found, this study still had important implications for the current body of knowledge.

The results of this research can be used by neurologist, psychiatrist, and psychologist tat treat patients for memory disorders. This study suggests that doctors and therapists may consider evaluating and correcting the sleeping patterns of their patients to improve short-term memory.

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